Relevant bibliographies by topics / Scene Text Recognition

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Scene Text Recognition'

Author: Grafiati
Published: 20 February 2023
Last updated: 31 July 2025

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Journal articles on the topic "Scene Text Recognition"

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Chen, Xiaoxue, Lianwen Jin, Yuanzhi Zhu, Canjie Luo, and Tianwei Wang. "Text Recognition in the Wild." ACM Computing Surveys 54, no. 2 (2021): 1–35. http://dx.doi.org/10.1145/3440756.

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The history of text can be traced back over thousands of years. Rich and precise semantic information carried by text is important in a wide range of vision-based application scenarios. Therefore, text recognition in natural scenes has been an active research topic in computer vision and pattern recognition. In recent years, with the rise and development of deep learning, numerous methods have shown promising results in terms of innovation, practicality, and efficiency. This article aims to (1) summarize the fundamental problems and the state-of-the-art associated with scene text recognition,
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Ratnamala, S. Patil, Hanji Geeta, and Huded Rakesh. "Enhanced scene text recognition using deep learning based hybrid attention recognition network." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4927–38. https://doi.org/10.11591/ijai.v13.i4.pp4927-4938.

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The technique of automatically recognizing and transforming text that is present in pictures or scenes into machine-readable text is known as scene text recognition. It facilitates applications like content extraction, translation, and text analysis in real-world visual data by enabling computers to comprehend and extract textual information from images, videos, or documents. Scene text recognition is essential for many applications, such as language translation and content extraction from photographs. The hybrid attention recognition network (HARN), unique technology presented in this researc
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Patil, Ratnamala S., Geeta Hanji, and Rakesh Huded. "Enhanced scene text recognition using deep learning based hybrid attention recognition network." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4927. http://dx.doi.org/10.11591/ijai.v13.i4.pp4927-4938.

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<span lang="EN-US">The technique of automatically recognizing and transforming text that is present in pictures or scenes into machine-readable text is known as scene text recognition. It facilitates applications like content extraction, translation, and text analysis in real-world visual data by enabling computers to comprehend and extract textual information from images, videos, or documents. Scene text recognition is essential for many applications, such as language translation and content extraction from photographs. The hybrid attention recognition network (HARN), unique technology
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Zhao, Qingyang. "Researches advanced in Natural Scenes Text Detection Based on Deep Learning." Highlights in Science, Engineering and Technology 16 (November 10, 2022): 188–97. http://dx.doi.org/10.54097/hset.v16i.2500.

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The research on text detection and recognition in natural scenes is of great significance for obtaining information from scenes. Thanks to the rapid development of convolutional neural networks and the continuous proposal of scene text detection methods based on deep learning, breakthroughs have been made in the recognition accuracy and speed of scene texts. This paper mainly sorts, analyzes and summarizes the scene text detection method based on deep learning and its development. Firstly, the related research background and significance of scene text detection are discussed. Then, the second
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Gao, Yunze, Yingying Chen, Jinqiao Wang, and Hanqing Lu. "Semi-Supervised Scene Text Recognition." IEEE Transactions on Image Processing 30 (2021): 3005–16. http://dx.doi.org/10.1109/tip.2021.3051485.

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Singh, Ananta, and Dishant Khosla. "Text Localization and Recognition in Real-Time Scene Images." International Journal of Scientific Engineering and Research 3, no. 5 (2015): 123–25. https://doi.org/10.70729/15051502.

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Yu, Wenhua, Mayire Ibrayim, and Askar Hamdulla. "Scene Text Recognition Based on Improved CRNN." Information 14, no. 7 (2023): 369. http://dx.doi.org/10.3390/info14070369.

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Text recognition is an important research topic in computer vision. Scene text, which refers to the text in real scenes, sometimes needs to meet the requirement of attracting attention, and there is the situation such as deformation. At the same time, the image acquisition process is affected by factors such as occlusion, noise, and obstruction, making scene text recognition tasks more challenging. In this paper, we improve the CRNN model for text recognition, which has relatively low accuracy, poor performance in recognizing irregular text, and only considers obtaining text sequence informati
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Ahmed, Saad, Saeeda Naz, Muhammad Razzak, and Rubiyah Yusof. "Arabic Cursive Text Recognition from Natural Scene Images." Applied Sciences 9, no. 2 (2019): 236. http://dx.doi.org/10.3390/app9020236.

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This paper presents a comprehensive survey on Arabic cursive scene text recognition. The recent years’ publications in this field have witnessed the interest shift of document image analysis researchers from recognition of optical characters to recognition of characters appearing in natural images. Scene text recognition is a challenging problem due to the text having variations in font styles, size, alignment, orientation, reflection, illumination change, blurriness and complex background. Among cursive scripts, Arabic scene text recognition is contemplated as a more challenging problem due t
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Liu, Shuhua, Huixin Xu, Qi Li, Fei Zhang, and Kun Hou. "A Robot Object Recognition Method Based on Scene Text Reading in Home Environments." Sensors 21, no. 5 (2021): 1919. http://dx.doi.org/10.3390/s21051919.

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With the aim to solve issues of robot object recognition in complex scenes, this paper proposes an object recognition method based on scene text reading. The proposed method simulates human-like behavior and accurately identifies objects with texts through careful reading. First, deep learning models with high accuracy are adopted to detect and recognize text in multi-view. Second, datasets including 102,000 Chinese and English scene text images and their inverse are generated. The F-measure of text detection is improved by 0.4% and the recognition accuracy is improved by 1.26% because the mod
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Shiravale, Sankirti Sandeep, Jayadevan R, and Sanjeev S. Sannakki. "Recognition of Devanagari Scene Text Using Autoencoder CNN." ELCVIA Electronic Letters on Computer Vision and Image Analysis 20, no. 1 (2021): 55–69. http://dx.doi.org/10.5565/rev/elcvia.1344.

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Scene text recognition is a well-rooted research domain covering a diverse application area. Recognition of scene text is challenging due to the complex nature of scene images. Various structural characteristics of the script also influence the recognition process. Text and background segmentation is a mandatory step in the scene text recognition process. A text recognition system produces the most accurate results if the structural and contextual information is preserved by the segmentation technique. Therefore, an attempt is made here to develop a robust foreground/background segmentation(se
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Dissertations / Theses on the topic "Scene Text Recognition"

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Sabir, Ahmed. "Enhancing scene text recognition with visual context information." Doctoral thesis, Universitat Politècnica de Catalunya, 2020. http://hdl.handle.net/10803/670286.

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This thesis addresses the problem of improving text spotting systems, which aim to detect and recognize text in unrestricted images (e.g. a street sign, an advertisement, a bus destination, etc.). The goal is to improve the performance of off-the-shelf vision systems by exploiting the semantic information derived from the image itself. The rationale is that knowing the content of the image or the visual context can help to decide which words are the correct andidate words. For example, the fact that an image shows a coffee shop makes it more likely that a word on a signboard reads as Dunkin
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Wang, Kewei. "Multilingual text-image recognition based on zero real sample learning." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29579.

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Scene text recognition (STR), derived from optical character recognition (OCR), has been extensively studied and made marvelous achievements in the past decades. While great progress has been made in majority languages such as Chinese and English, however, for most of the minority languages, the exceptional lack of annotated text databases for training purposes is always exists. Thus, the paper aims to enhance the overall performance of multilingual STR models for minority languages. We strictly choose Japanese as a target minority language and build a novel STR model. For text detection, w
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Fei, Xiao Lei. "Hybrid segmentation on slant & skewed deformation text in natural scene images." Thesis, University of Macau, 2010. http://umaclib3.umac.mo/record=b2182857.

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Saracoglu, Ahmet. "Localization And Recognition Of Text In Digital Media." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12609028/index.pdf.

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Textual information within digital media can be used in many areas such as, indexing and structuring of media databases, in the aid of visually impaired, translation of foreign signs and many more. This said, mainly text can be separated into two categories in digital media as, overlay-text and scene-text. In this thesis localization and recognition of video text regardless of its category in digital media is investigated. As a necessary first step, framework of a complete system is discussed. Next, a comparative analysis of feature vector and classification method pairs is presented. Furtherm
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Hsieh, Ming-Yuan, and 謝明遠. "Scene Text Detection and Recognition in Video Frames." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/69051880149052992560.

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碩士<br>大葉大學<br>資訊管理學系碩士班<br>100<br>People sometimes need to focus on the specific something, and they are unable to take into account the surrounding environment. Example: driving car. In driving car, people need to focus in front of traffic conditions, and they are easy to overlook road sign. Help people notice the sign’s message with using computer vision technology. This paper major research includes learning skew text and text recognition in continuous frame. In the scene image, text almost is skew. So the result is bad in using document ORC. We let OCR kernel recognition text by learning s
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Li, Hui. "Text detection and recognition in natural scene images." Thesis, 2018. http://hdl.handle.net/2440/115175.

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This thesis addresses the problem of end-to-end text detection and recognition in natural scene images based on deep neural networks. Scene text detection and recognition aim to find regions in an image that are considered as text by human beings, generate a bounding box for each word and output a corresponding sequence of characters. As a useful task in image analysis, scene text detection and recognition attract much attention in computer vision field. In this thesis, we tackle this problem by taking advantage of the success in deep learning techniques. Car license plates can be viewe
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Kumar, Deepak. "Methods for Text Segmentation from Scene Images." Thesis, 2014. http://etd.iisc.ac.in/handle/2005/2693.

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Recognition of text from camera-captured scene/born-digital images help in the development of aids for the blind, unmanned navigation systems and spam filters. However, text in such images is not confined to any page layout, and its location within in the image is random in nature. In addition, motion blur, non-uniform illumination, skew, occlusion and scale-based degradations increase the complexity in locating and recognizing the text in a scene/born-digital image. Text localization and segmentation techniques are proposed for the born-digital image data set. The proposed OTCYMIST technique
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Kumar, Deepak. "Methods for Text Segmentation from Scene Images." Thesis, 2014. http://etd.iisc.ernet.in/handle/2005/2693.

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Recognition of text from camera-captured scene/born-digital images help in the development of aids for the blind, unmanned navigation systems and spam filters. However, text in such images is not confined to any page layout, and its location within in the image is random in nature. In addition, motion blur, non-uniform illumination, skew, occlusion and scale-based degradations increase the complexity in locating and recognizing the text in a scene/born-digital image. Text localization and segmentation techniques are proposed for the born-digital image data set. The proposed OTCYMIST technique
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Dmitry, Ladeev, and LadeevDmitry. "3D Effects Elimination Towards Text Recognition In Natural Scene Images." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/60952898460411219561.

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碩士<br>亞洲大學<br>資訊工程學系碩士班<br>94<br>3D text recognition is an area that develops fast in the past years due to the great improvement in the computational capabilities of hand devises and computer systems, and the increase in the imaging quality of digital cameras. 3D text recognition using digital cameras is different from traditional 2D text recognition using scanners in many ways. 3D effects have to be considered and eliminated before the performing of 3D text recognition.Hence, new methodologies have to be developed for 3D text recognition. This work is devoted to the preprocessing part of 3D
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Weinman, Jerod J. "Unified detection and recognition for reading text in scene images." 2008. https://scholarworks.umass.edu/dissertations/AAI3325128.

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Although an automated reader for the blind first appeared nearly two-hundred years ago, computers can currently "read" document text about as well as a seven-year-old. Scene text recognition brings many new challenges. A central limitation of current approaches is a feed-forward, bottom-up, pipelined architecture that isolates the many tasks and information involved in reading. The result is a system that commits errors from which it cannot recover and has components that lack access to relevant information. We propose a system for scene text reading that in its design, training, and operation
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Books on the topic "Scene Text Recognition"

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Ahmed, Saad Bin, Muhammad Imran Razzak, and Rubiyah Yusof. Cursive Script Text Recognition in Natural Scene Images. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1297-1.

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Ahmed, Saad Bin, Muhammad Imran Razzak, and Rubiyah Yusof. Cursive Script Text Recognition in Natural Scene Images: Arabic Text Complexities. Springer, 2019.

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Ahmed, Saad Bin, Muhammad Imran Razzak, and Rubiyah Yusof. Cursive Script Text Recognition in Natural Scene Images: Arabic Text Complexities. Springer Singapore Pte. Limited, 2021.

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Vanmai, Jean. Jean Vanmai’s Chân Đăng The Tonkinese of Caledonia in the colonial era. Translated by Tess Do and Kathryn Lay-Chenchabi. University of Technology, Sydney, 2022. http://dx.doi.org/10.5130/aai.

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Jean Vanmai’s Chân Đăng The Tonkinese of Caledonia in the colonial era is a rare insider’s account of the life experiences of Chân Đăng, the Vietnamese indentured workers who were brought from Tonkin to work in the New Caledonian nickel mines in the 1930s and 1940s, when both Indochina and New Caledonia were French colonies. Narrated from the unique perspective of a descendant of Chân Đăng, the novel offers a deep understanding of how Vietnamese migration, shaped by French colonialism and the indenture system, led to the implantation of the Vietnamese community in New Caledonia, in spite of th
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Book chapters on the topic "Scene Text Recognition"

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Huang, Huijuan, Zhi Tian, Tong He, Weilin Huang, and Yu Qiao. "Orientation-Aware Text Proposals Network for Scene Text Detection." In Biometric Recognition. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69923-3_79.

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Shi, Haodong, Liangrui Peng, Ruijie Yan, Gang Yao, Shuman Han, and Shengjin Wang. "Mask Scene Text Recognizer." In Document Analysis and Recognition – ICDAR 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86337-1_3.

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Liang, Shiqi, Ning Bi, and Jun Tan. "Scene Text Recognition: An Overview." In Pattern Recognition and Artificial Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09037-0_27.

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Ahmed, Saad Bin, Muhammad Imran Razzak, and Rubiyah Yusof. "Foundations of Cursive Scene Text." In Cursive Script Text Recognition in Natural Scene Images. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1297-1_1.

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Fujitake, Masato. "JSTR: Judgment Improves Scene Text Recognition." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-66329-1_13.

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Deng, En, Gang Zhou, Jiakun Tian, Yangxin Liu, and Zhenhong Jia. "Text Enhancement: Scene Text Recognition in Hazy Weather." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-41731-3_8.

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Zhang, Wenqing, Yang Qiu, Minghui Liao, Rui Zhang, Xiaolin Wei, and Xiang Bai. "Scene Text Detection with Scribble Line." In Document Analysis and Recognition – ICDAR 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86337-1_6.

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Li, Chun, Hongjian Zhan, Kun Zhao, and Yue Lu. "Thai Scene Text Recognition with Character Combination." In Pattern Recognition and Computer Vision. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18913-5_25.

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Zhang, Ziyin, Lemeng Pan, Lin Du, Qingrui Li, and Ning Lu. "CATNet: Scene Text Recognition Guided by Concatenating Augmented Text Features." In Document Analysis and Recognition – ICDAR 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86549-8_23.

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Sun, Xurui, Jiahao Lyu, Yifei Zhang, et al. "Feature Enhancement with Text-Specific Region Contrast for Scene Text Detection." In Pattern Recognition and Computer Vision. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8540-1_1.

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Conference papers on the topic "Scene Text Recognition"

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wang, jiahao, yahao lu, and lianpei wu. "Advanced scene text recognition and application for dynamic driving scenes." In 4th International Conference on Signal Image Processing and Communication, edited by Xianye Ben and Lei Chen. SPIE, 2024. http://dx.doi.org/10.1117/12.3041890.

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Liang, Min, Jia-Wei Ma, Xiaobin Zhu, Jingyan Qin, and Xu-Cheng Yin. "LayoutFormer: Hierarchical Text Detection Towards Scene Text Understanding." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.01483.

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Cai, Zhaojiang, Enqi Zhan, Sui Lei, Yu Wang, and Jian Zhou. "Chromatic Rectification Network for Scene Text Recognition." In 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE, 2024. http://dx.doi.org/10.1109/icsp62122.2024.10743472.

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Ying, Xin, Raja Kumar Murugesan, Siva Raja Sindiramutty, et al. "Scene Text Recognition using Deep Learning Techniques." In 2024 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC). IEEE, 2024. https://doi.org/10.1109/etncc63262.2024.10767484.

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Ren, Jinbiao, Tao Deng, Yanlin Huang, Da Qu, Jianqiu Su, and Bingen Li. "Compressed Vision Transformer for Scene Text Recognition." In 2024 7th International Conference on Algorithms, Computing and Artificial Intelligence (ACAI). IEEE, 2024. https://doi.org/10.1109/acai63924.2024.10899477.

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Yu, Haiyang, Xiaocong Wang, Bin Li, and Xiangyang Xue. "Orientation-Independent Chinese Text Recognition in Scene Images." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/185.

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Scene text recognition (STR) has attracted much attention due to its broad applications. The previous works pay more attention to dealing with the recognition of Latin text images with complex backgrounds by introducing language models or other auxiliary networks. Different from Latin texts, many vertical Chinese texts exist in natural scenes, which brings difficulties to current state-of-the-art STR methods. In this paper, we take the first attempt to extract orientation-independent visual features by disentangling content and orientation information of text images, thus recognizing both hori
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Nakamura, Toshiki Nakamura, Anna Zhu, and Seiichi Uchida. "Scene Text Magnifier." In 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2019. http://dx.doi.org/10.1109/icdar.2019.00137.

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Chen, Jingye, Bin Li, and Xiangyang Xue. "Scene Text Telescope: Text-Focused Scene Image Super-Resolution." In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021. http://dx.doi.org/10.1109/cvpr46437.2021.01185.

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Nakamura, Toshiki, Anna Zhu, Keiji Yanai, and Seiichi Uchida. "Scene Text Eraser." In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2017. http://dx.doi.org/10.1109/icdar.2017.141.

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Kuo, Chi-Ling, Jen-Chun Lee, Chung-Hsien Chen, Chiu-Chin Lin, and Chia-Ti Wu. "Traditional Chinese scene text recognition." In 2022 IET International Conference on Engineering Technologies and Applications (IET-ICETA). IEEE, 2022. http://dx.doi.org/10.1109/iet-iceta56553.2022.9971491.

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